Population genetic polymorphisms of pharmacogenes in Zimbabwe, a potential guide for the safe and efficacious use of medicines in people of African ancestry : Pharmacogenetics and Genomics

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Population genetic polymorphisms of pharmacogenes in Zimbabwe, a potential guide for the safe and efficacious use of medicines in people of African ancestry

Mbavha, Bianza T.a,*; Kanji, Comfort R.a,*; Stadler, Nadinab; Stingl, Juliac; Stanglmair, Andreab; Scholl, Catharinab; Wekwete, Williamd; Masimirembwa, Collena

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Pharmacogenetics and Genomics 32(5):p 173-182, July 2022. | DOI: 10.1097/FPC.0000000000000467

Abstract

Introduction

Therapeutics are the backbone of disease prevention, management and treatment. The use of therapeutics is however associated with adverse drug reactions (ADRs) or lack of efficacy in some patients when given for the same therapeutic indication and at the same dose. This is mainly because of the existing interindividual and interpopulation variabilities in drug response, affecting the safety and efficacy of medicines [1]. ADRs are one of the leading causes of morbidity and mortality all over the world. It is estimated that approximately 2 million hospitalized patients in the USA develop serious ADRs, which may lead to expenses of about 242 billion US Dollars [2]. Some research studies in Africa reported that 1.5–6.3% of all hospital admissions were due to ADRs, and a large proportion (6.3–49.5%) of hospitalized patients acquired ADRs during their hospital stay [3,4]. The use of antiretroviral drugs in the management of HIV infections in Africa has resulted in increased ADRs, accounting for about 80% of all recorded ADRs in sub-Saharan Africa [5]. The WHO pharmacovigilance database, Vigibase, shows that for most drugs, African populations have higher ADRs incidences than the rest of other world populations [6].

There are many factors known to affect the safety and efficacy of drugs, with genetic interindividual variability having the greatest effect for most drugs [7]. Variations in genes that code for drug metabolizing enzymes, transporters and drug targets have been shown to influence drug safety and efficacy. Genetic polymorphisms account for about 35% of potential ADRs through drug–gene interactions (DGIs) and drug–drug–gene interactions [8]. This has given rise to the field of PGx as the ‘lowest hanging fruit’ in delivering the promise of Precision Medicine. Regulatory authorities such as European Medicines Agency [9] and Food and Drug Administration (FDA) [10] now include PGx information in over 150 product labels as either information or prescription guidelines. Pharmacogenetics experts have also developed clinical guidelines such as Clinical Pharmacogenetics Implementation Consortium (CPIC), the Dutch Pharmacogenetics Working Group guidelines [11], the Canadian Pharmacogenomics Network for Drug Safety [12] and the French National Network (Réseau) of Pharmacogenetics [11] to help clinicians in drug and dose selection when prescribing drugs. There are currently no clinical pharmacogenetics guidelines for use of medicines in any African population.

In Europe, America and Asia, there are now numerous multisite national and international initiatives to implement PGx-guided pharmacotherapy [13]. Little is however being done in Africa, which despite having a population of 1.2 billion people in 54 countries [14], the largest genomic diversity and having the highest burden of ADRs [15], it only contributes less than 1.27% of global pharmacogenomic research and less than 1% of clinical research [14]. This is why in most cases, ADRs that are African-specific are only discovered after a drug has already been released on the market [16]. The relatively few studies done on Africans are, however, revealing important observations. First, the pharmacogenetic variation among African populations is much higher than that in Caucasians and Asians [15]. Second, African populations have either novel and unique variants or significantly different allele frequencies for some genes compared with other populations [15,17,18]. There are also certain variants that are found in other world populations but absent or rare in African populations [17].

In response to this PGx gap in Africa, we have been working on genetic polymorphisms of pharmacogenes and exploring DGIs potentially important for Africans. This work has culminated in the design of a pharmacogenetic testing open array and accompanying pharmacokinetic prediction algorithms called GenoPharm (Thermo Fisher Scientific, Waltham, Massachusetts, USA), which can predict clinical response for over 100 medicines on the market [19]. The GenoPharm (Thermo Fisher Scientific) panel covers 120 pharmacogenetic variants, inclusive of some variants that are unique to people of African ancestry such as CYP2D6*17 [20], CYP2D6*29 [21] and CYP2B6*18 [22]. The panel also includes variants that are clinically actionable, with PharmGKB 1A or 1B levels of clinical significance. In this study, the GenoPharm (Thermo Fisher Scientific) array was used to provide data on population frequency of PGx variants in Zimbabwe, in context of the most commonly used drugs in the country.

Materials and methods

Sampling and processing

A total of 522 black Zimbabwean healthy volunteers aged 18–30 years were enrolled for this study (11% females), each with a written informed consent for participation in the study. The study was approved by the Medical Research Council of Zimbabwe and Medicines Control Authority of Zimbabwe (MCAZ). From each participant, 4 ml of peripheral blood was collected in an EDTA tube, and 200 µl of the whole blood was used for genomic DNA extraction using Kingfisher flex Magnetic Particle Processor (Thermo Fisher Scientific, Waltham, Massachusetts, USA). DNA was quantified using the Qubit 4 fluorometer (Thermo Fisher Scientific). For copy number assays, aliquots of the DNA samples were normalized to a concentration of 5 ng/µl using nuclease free water.

Single nucleotide polymorphism genotyping

TaqMan-based single nucleotide polymorphism genotyping was performed using TaqMan PCR reagents from Applied Biosystems (San Diego, California, USA). Using a 120-assay Open Array GenoPharm PGx Panel (Thermo Fisher Scientific; see Table, Supplemental Digital Content 1, https://links.lww.com/FPC/B420), 19 pharmacogenes were genotyped, including eight phase I drug metabolizing enzymes (seven of them belonging to the Cytochrome P450 family), five phase II enzymes and three drug transporters. Real-time PCR was done using the QuantStudio 12K Flex real-time PCR system (Thermo Fisher Scientific), according to the manufacturers’ protocol.

Copy number variation assay

CYP2D6 copy number was determined using the Applied Biosystems TaqMan copy number variation (CNV) assays targeting Exon 9 (assay ID: Hs00010001_cn). A duplex real-time PCR reaction was done with each run containing both the CYP2D6 assay and the TaqMan Copy number reference assay (ribonuclease P) as the reference gene. The copy number assays for each sample including the no template control were run in quadruplicate in a 96-well reaction plate. Each reaction had a total volume of 20 µl with 4 µl of 5-ng/µl normalized gDNA and 16 µl of the master mix. The qPCR was run on the QuantStudio 12K Flex real-time PCR system. The CNV platform used in this study cannot do phasing, so the genotypes that had a CYP2D6 copy number ≤2 were included in the genotype analysis.

Genotype data analysis

Genotype data analysis was done according to Thermofisher open-array PGx bioinformatic processing. The run files were first analysed in QuantStudio 12K flex software and then exported to the TaqMan Genotyper software where further data analysis was done. CYP2D6 copy number raw results were also analysed first in the QuantStudio 12K flex software and then in the Copy Caller software to determine the copy numbers. Further analysis of the genotyping and copy number results was done using the Allele Typer software that is available online. PharmVar, PharmGKB as well as CPIC allele functionality tables were used to interpret the genotypes into phenotypes that are of clinical relevance.

Statistical analysis

Allele and genotype frequencies for the genes tested were estimated from the results obtained. The genotypes observed were matched to Hardy–Weinberg equilibrium (HWE) expectation. The difference between the observed and expected genotype frequencies was estimated using the Chi-square (X2) test, with a P-value ≤ 0.05 indicating deviation from HWE. The allele frequency differences across populations were estimated using the Chi-square test.

Selection of clinically actionable drug–gene interactions

The frequency of clinically actionable variants carried by the participants enrolled was determined. Variants were considered actionable if they are listed as actionable according to CPIC or PharmGKB with levels 1A or 1B. Table 1 shows the actionable variants for the genes included in this study.

Table 1 - Clinically actionable variants that were observed for genes included in this study
Gene symbol Actionable variants
CYP2B6 *4; *6; *18
CYP2C9 *2; *6; *8; *11
CYP2C19 *2; *17
CYP2D6 *4; *5; *10; *11; *17; *29; *41
CYP3A5 *3; *6; *7
CYP4F2 *3
TPMT *3C
DPYD *2A
Variants were considered actionable if they are listed as actionable according to CPIC or PharmGKB with levels 1A or 1B.

Data procurement

Data on the medicines used in Zimbabwe was extracted from the Essential Drug’s List of Zimbabwe (EDLIZ) [15] in its therapeutic areas. The therapeutic areas were grouped according to the associated biomarkers based on drug metabolizing enzymes involved in the disposition of the medicines. Data on the national drug procurement levels for drugs that are procured by the regulatory board in Zimbabwe were obtained from MCAZ and were used to estimate the relevance of PGx guided dosing in Zimbabwe.

Results

Study population

Patient baseline characteristics are provided in Table 2. The average age of participants enrolled was 24 years, with 11% females and 89% males. The majority of the study participants were of the Shona ethnic group (97%). The highest number of the participants came from the Mashonaland East (33%) and Manicaland (25%) provinces, out of the 10 provinces in Zimbabwe.

Table 2 - Study participants baseline characteristics
Demographics N % Value
Sex
Male 413 89
Female 52 11
Race
Black 465 100
Ethnicity
Ndebele 11 2
Nyanja 1 <1
Shona 451 97
Other 2 <1
Not declared 1 <1
Province of origin
Harare 18 4
Manicaland 115 25
Mashonaland Central 41 9
Mashonaland East 154 33
Mashonaland West 35 8
Masvingo 44 9
Matabeleland North 3 1
Matabeleland South 7 2
Midlands 28 6
Age (years)
Mean 24
SD 5
Median 22

Genotype and phenotype frequencies

The genotype frequencies were subjected to HWE analysis using the Chi-square test (see Table, Supplemental Digital Content 2, https://links.lww.com/FPC/B420). All of the genotype frequencies were found to be in HWE except for G6PD and Retinoic Acid Receptor Gamma (RARG), which had P-values less than 0.05. Phenotypes for all the genotypes obtained were predicted according to the allele functionality and the diplotype to phenotype tables on CPIC [23] and from other publicly available databases (PharmVar, PharmGKB). Figure 1 represents the predicted phenotype frequencies of CYP450 pharmacogenes. The figure shows that CYP2B6 and CYP3A5 had the most significant number of poor metabolizers (PMs) and intermediate metabolizers (IMs) with frequencies of 23.9 and 26.9% for PMs and 49.5 and 45.7% for IMs, respectively. CYP2C19 gene had the highest number of rapid metabolizers (22.8%) and ultrarapid metabolizers (UMs) (2.9%).

F1
Fig. 1:
Predicted phenotype frequencies for CYP450 pharmacogenes in Black Zimbabwean population.

Allele frequencies

Allele frequencies for the genes included in this study were determined (see Table, Supplemental Digital Content 3, https://links.lww.com/FPC/B420), and the allele frequencies for CYP450 genes were compared with those previously reported for Africans, Eastern and Southern Asians, Americans and Europeans, and the results are summarized in Table 3. Several variants showed significant differences among populations and were more frequent among the African populations when compared with Americans, Europeans and Asians. For example, CYP2B6*18 (10.5%), CYP2D6*17 (15%) and *29 (9.5%) had higher allele frequencies in this study and in other Africans compared with Asians, Europeans and Americans (P < 0.05). The allele frequency of CYP3A4*1B was also higher, at 76.8%, which is in concordance with that reported for Africans (76.5%) studied in the 1000 Genomes Project, phase 3. The CYP4F2*3 variant had significant allele differences across populations; with 5.2% for Zimbabweans in this study, 41% Southern Asians, 29% for Europeans and 24% for Americans (P < 0.0001). The CYP2C19*2 and *17 frequencies were 16.1 and 16.7% in this study and is in concordance with other Africans: 17% (P = 0.7901) and 23.5% (P = 0.1140), respectively.

Table 3 - Population allele frequencies for CYP450 genes, comparison between frequencies obtained in this study and those for other Africans, Eastern Asians, South Asians, Europeans and Americans
Gene Allele This study Africans Eastern Asians South Asians Europeans Americans
CYP2B6 *4a,b,c,e 0.0201 0.3553 0.2655 0.3775 0.3327
*5 0.0067 0.0113 0.003 0.089 0.1123 0.072
*6 0.3774 0.3744 0.2153 0.3814 0.2356 0.3732
*7 0.0038 NA NA NA NA NA
*18e 0.1054 0.0825 0 0 0 0.0101
CYP2D6 *2 0.1850 0.1557 0.12 0.29 0.2765 0.2208
*2A 0.0173 0.0063 0.0195 0.0819 0.0908 0.0859
*5ae 0.1156 0.0539 0.0486 0.0459 0.0295 0.0159
*10 0.0318 0.1127 0.5714 0.1646 0.2018 0.1484
*17be 0.1503 0.2179 0 0 0.002 0.0086
*29be 0.0954 0.1074 0 0 0 0.0029
*41 0.0087 0.0182 0.0377 0.1217 0.0934 0.062
CYP2C9 *2 0.0010 0.0083 0.0010 0.0348 0.1243 0.0994
*3 0.0029 0.0023 0.0337 0.1094 0.0726 0.0375
*4 0.0010 0.0000 0.0000 0.0000 0.0000 0.0000
*5be 0.0153 0.0166 0 0 0 0.0014
*6be 0.0048 0.0109 0 0 0 0.001
*8be 0.0977 0.053 0 0.001 0.002 0.0014
*11be 0.0307 0.0242 0 0.001 0.002 0.0014
CYP2C19 *2b,c 0.1609 0.1702 0.3125 0.3579 0.1451 0.1052
*3 0.0019 0.0023 0.0556 0.0123 0.0000 0.0000
*10 0.0010 0.0015 0 0 0 0.0014
*17 0.1667 0.2352 0.0149 0.136 0.2237 0.1196
CYP3A5 *3be 0.1775 0.18 0.7133 0.6677 0.9433 0.7968
*6be 0.1833 0.1543 0 0 0.003 0.0231
*7be 0.1363 0.118 0 0 0 0.0029
CYP3A4 *1Bbe 0.7682 0.7655 0.004 0.0399 0.0278 0.1052
CYP4F2 *3b–e 0.0517 0.0825 0.2143 0.4131 0.2903 0.2378
Allele frequencies for other populations were obtained from the 1000 Genome Project. For CYP2B6*4, the frequency was obtained from the gnomAD study, version 3.
NA, not applicable.
The letters a–e were used if there is any statistical significance (P < 0.05) of the allele frequency obtained in this study and the population compared to
aAfricans,
bEastern Asians;
cSouth Asians;
dEuropeans and
eAmericans.

Clinically actionable variants

Next, we determined the frequency of actionable pharmacogenomic variants (see Table 1). Ninety-nine percent of the participants carried at least one actionable variant, with a high proportion of participants (38%) carrying at least three actionable variants, as shown in Fig. 2.

F2
Fig. 2:
The pharmacogenomic clinically actionable variants that were observed in the black Zimbabwean population.

Data procurement

Medicines on the EDLIZ that have pharmacogenomic information in the FDA product labels were obtained within their respective therapeutic areas according to EDLIZ [24], and they were grouped according to their associated pharmacogenomic biomarkers, as shown in Fig. 3. CYP2D6 and G6PD were the most common pharmacogenomic biomarkers, associated with metabolism of the highest numbers of essential drugs used in Zimbabwe (16 and 14 drugs, respectively) out of the 57 drugs that were analysed in this study. To put into perspective the importance of these biomarkers on the Zimbabwean population, data on the national procurement levels of these drugs were assembled and summarized in Fig. 4. Over 578 million (8.76 in log10 scale) doses of glibenclamide and about 23 million (7.36 in log10 scale) of isoniazid were procured in Zimbabwe for the years 2018 and 2019.

F3
Fig. 3:
The biomarkers associated with metabolism of most of the essential drugs used in Zimbabwe.
F4
Fig. 4:
The number of drugs procured by Medicines Control Authority of Zimbabwe (MCAZ) for the years 2018 and 2019. The unit measure for each drug is illustrated using log10 scale.

Discussion

This is the first study to genotype clinically actionable pharmacogenes in an African population using an open-array platform of 120 genetic variants so as to estimate the potential importance of pharmacogenetics in the efficacy and safety of medicines commonly used in Africa. Although the study does not well represent the African population across the continent, the results obtained were in concordance with previous findings from sparse single or few genetic variants studies done in a number of sub-Saharan African populations [15]. By securing consent to revisit genotyped volunteers, the study has created a clinical pharmacogenetics research resource for the conduct of clinical drug–gene interaction studies for new or repurposed medicines.

The Zimbabwean population consists of 98% native African Bantu-speaking groups, with the Shona (70%) and Ndebele (20%) being major ethnicities. However, there are also other minority Bantu-speaking groups. Most of our study participants belonged to the Shona ethnic group (89%). This is mainly because recruitment was done in Harare, the capital city of Zimbabwe that is located in the Shona speaking region of the country. The study, however, represents a high number of individuals tested and number of genes and genetic variants tested in each individual. This is enabling us to better estimate genetic frequencies in a more complete data set than previous efforts where we had to collate sparse data from studies with few individuals, less genes and less genetic variants tested. The frequency of each genotype was evaluated for HWE, and only G6PD and RARG did not obey this equilibrium. While the reasons for this might not be clear for RARG, for G6PD, it might be because the evolution of this gene was due to malaria-induced selective pressure [17].

The high frequency of CYP2C19 UMs was as a result of the *17 allele, which has been associated with increased bleeding for patients on clopidogrel [25–27], hence need for further characterization to improve efficacious use of medicines. The observed frequency of 16.7% for CYP2C19*17 is comparable with what has been observed in the Ethiopian population, of 18% [28]. CYP2C19*2 was observed at a frequency of 16.1% in this study. The *2 allele is associated with a decrease in the response of platelets to the clopidogrel drug [29], increasing the risk of the recurrence of major adverse cardiovascular events [26]. We have also shown a major role of CYP2C19 in the metabolism of the schistosomicide drug, praziquantel [30] and the high frequency of CYP2C19 UM might contribute to the high clearance of this drug. We also observed a high frequency of CYP2C9 gene variants *8 (9.8%) and *11 (3.1%), which are associated with the poor metaboliser phenotype that has implication on warfarin dosing [31]. Use of PGx in warfarin dosing has the potential to reduce ADRs, cost of treatment and improve efficacy. A pharmacoeconomic study in the USA concluded that PGx-guided warfarin dosing was cost-effective compared with clinically guided dosing [32].

CYP2B6 PM genotypes had high frequencies, owing to the *6 and *18 alleles. These alleles have been comprehensively reported for the Zimbabwean population [33–35]. CYP2B6 is associated with metabolism of about 10–12% of all commercially available drugs. The *6 and *18 alleles lead to an increase in individual active drug exposure and toxicity, leading to higher risks of ADRs [22].

Due to the high HIV burden in sub-Saharan Africa, there has been an increase in antiretroviral treatment-related ADRs accounting for about 80% of the reported ADRs. Efavirenz and nevirapine, which are CYP2B6 substrates, were the most reported drug-induced adverse events [36]. The standard dose of efavirenz at 600 mg resulted in high efavirenz exposure levels in carriers of CYP2B6*6 in Africa, which resulted in neuropsychiatric adverse events. This led to the development of a genotype-guided dose adjustment algorithm for efavirenz [34], leading to the standard dose adjustment to 400 mg by WHO [37], and Zimbabwe was one of the first countries to adopt the reduced doses of efavirenz. HIV patients in Africa also suffer the burden of Kaposi sarcoma; Kaposi sarcoma occurs at a high-frequency I Africa (2–20%) in HIV patients [38] though its prevalence has significantly reduced in Zimbabwe [39] since the introduction of antiretroviral therapy [40]. Doxorubicin is one of the many drugs used in the treatment of Kaposi sarcoma but has been associated with cardiotoxicity. Certain genetic variations of RARG and UGT1A6 have been shown to be associated with anthracycline-induced cardiotoxicity (ACT) [41,42]. High ACT incidences of 21–28% have already been reported in Africa [43], and in this study, we observed high frequencies of the ACT-associated risk variants; RARG (rs2229774A) and UGT1A6*4 at frequencies of 34.1 and 13.3%, respectively.

A very high frequency of the CYP3A4*1B (76.8%) was observed in this study (Table 3), which is in concordance with previously reported high frequencies of 76% in other African populations [44]. The CYP3A4*1B allele has been associated with an approximately 10-fold increase in the risk for prostate cancer in African American men [45] and an increased risk of invasive ovarian cancer in homozygous women [46].

Among all the pharmacogenes in this study, CYP2D6 was the most polymorphic gene. The enzyme coded by this gene is responsible for metabolism of over 25% of registered drugs [47]. It is mainly involved in the metabolism of antidepressants, antipsychotics and antiarrhythmics classes of drugs [48]. The CYP2D6 alleles *17 [38] and *29 [21], which result in a decreased enzymatic activity, are specific to the African population. The *17 allele was observed at a frequency of 15% in this study, which is in agreement with our first report [20], but much higher than 0–0.9% allele frequencies observed in Asian and Caucasian populations (see Table 3). An allele frequency of 10.9% was observed for the *29 allele, which is also high compared with the 0–0.1% frequencies in Asians and Caucasians [15]. The *5 allele results in a nonfunctional enzyme [49], and it was observed in this study at a frequency of 11.6%. The presence of reduced function or nonfunctional CYP2D6 variants is associated with an increased risk of breast cancer recurrence in patients taking tamoxifen [50]. There is a great need for conducting clinical studies in people of African ancestry to understand the impact of using Caucasian-based dose regimens without adjusting them to fit the genetic makeup of Africans.

As observed, 100% of the study population carry at least one actionable PGx variant and a high proportion of the participants carried at least three actionable variants. These results are consistent with what has been observed in other populations: 99.6 % HK Chinese [51], 98 % Malays and Indians [52] and 99% Americans and Europeans [53], who carry at least one actionable variant.

The G6PD deficiency variant (rs1050829) was significant among the black Zimbabweans included in this study, at a frequency of 33.3%. This is because G6PD deficiency has been found to be common in African populations, and it is postulated to have arisen due to pressure from malaria infection [17]. Individuals with this deficiency are at risk for haemolytic anaemia that can be triggered by infections, certain foods or medications [54]. G6PD deficiency is an X-linked disorder, and in this study, we observed 22% of the males and 12.2% of the females having the deficiency. One of the drugs that trigger haemolysis in G6PD-deficient individuals is primaquine [55] because of the inability to detoxify reactive metabolites produced during primaquine metabolism. Chloroquine and hydroxychloroquine also increase the risk of haemolysis, and warnings on the use of these drugs on G6PD-deficient individuals have been issued by FDA [54].

After compiling pharmacogenomic data for medicines under frequent use in Zimbabwe, CYP2D6 and G6PD were found to be the most common biomarkers, associated with metabolism of most essential drugs of Zimbabwe (Fig. 3). Considering the frequency of the G6PD deficiency in Zimbabwe and the frequencies of CYP2D6 *17 and *29, it shows the importance of developing clinical guidelines for safety and efficacy of drugs metabolized by these genes. Based on the national procurement levels of drugs in Zimbabwe (Fig. 4), large doses of drugs are procured without taking into consideration PGx factors that may affect their safety and efficacy in the Zimbabwean population. There is need for African countries like Zimbabwe to start integrating pharmacogenomics in pharmacovigilance programs. This way medicines with clinically significant DGIs could be identified, thus earmarking them for bridging studies to address any associated safety and efficacy issues.

In conclusion, this study has shown that African genetic variants show notable differences with those of European and Asian populations and are highly variable among different individuals [15,56]. This contributes to strengthening the recommendation of either including African populations in early clinical development of medicines or to conduct bridging studies for medicines already approved in other populations before they are introduced into African populations. In the process of generating these data, we have also built a clinical resource of a Zimbabwean cohort of genotyped healthy volunteers who can be recalled for the conduct of clinical trials in genotypes of interest.

Acknowledgements

This study was supported by the Global Health Protection (GHP) program (German) and the National Institutes of Health (NIH) funded National Human Genome Research Institute under Award Number U24HG006941. The contribution by Julia Stingl has been funded by the H2020 grant 668353 U-PGx.

Medicines Control Authority of Zimbabwe (MCAZ) and the Ministry of Health and Child Care (MoHCC) are acknowledged for providing data on national drug procurement quantities. Moira Mubani is sincerely acknowledged for providing technical support.

The datasets generated during and/or analysed during the current study are not publicly available, but are available from the corresponding author (Prof. Collen Masimirembwa) on reasonable request.

Conflicts of interest

There are no conflicts of interest.

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Keywords:

African ancestry; pharmacogenomics; open array; precision medicine; drug–gene interactions

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